Politecnio die Bari - Catalogo di prodotti della Ricerca
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Silicone 3D printing: Experimental validation of a reduced-order numerical model for optimal layer deposition
No full textSilicone 3D printing holds a significant potential in soft robotics, stretchable electronics, and custom medical devices. However, challenges such as inconsistent material deposition continue to limit printing precision. This study presents a reduced-order numerical model integrating material rheology, process parameters and nozzle geometry to optimize silicone extrusion. A support vector regression algorithm fine-tunes printing parameters to generate an inlet pressure profile that minimizes over- and under-extrusion and ensures precise layer contouring. Full three-dimensional simulations of non-Newtonian silicone deposition coupled with experimental validation confirm the model's accuracy in 3D printing a 90-degree rounded corner shape. The proposed approach yields more stable printing paths and reduces layer width variability to 4.21%, compared to 15.02% obtained with constant inlet pressure. Finally, the proposed model is applied to fabricate wavy and multi-strand paths, demonstrating enhanced accuracy and consistency
Development of bio-based flexible polyurethane foams incorporating phase change materials for thermal energy storage applications
No full textThe fabrication of innovative polyurethane panels for energy efficiency is increasingly important and should ideally be based on sustainable, non-fossil-based feedstock. In this context, the present work reports the development of sustainable composite panels by incorporating microencapsulated phase change materials (PCMs) into flexible polyurethane (PU) foams, synthesized from a polyol derived from waste cooking oil (WCO) and a partially bio-based isocyanate. The PU-PCM panels achieved energy storage capacity up to 26.2 J/g at a maximum PCM content of 15 phr. Uniform PCM dispersion slightly reduced cell size and increased panel density (from 128 to 157 kg/m3), thereby enhancing structural support and rigidity while reducing elasticity (compression force deflection up to 234.8 kPa). Fatigue tests confirmed resistance to cyclic loading, with increased dynamic stress and stiffness due to PCM integration. Differential scanning calorimetry showed minimal enthalpy hysteresis (+0.26 J/g) and a stable phase-change temperature (36 + 0.1 degrees C), demonstrating resilience to thermal and mechanical stress. Thermal conductivity increased slightly (from 46.15 to 48.44 mW/m & sdot;K at 20 degrees C) due to the silica-based PCM shell, while thermal diffusivity decreased, favouring transient thermal regulation. Fire performance remained unaffected, likely due to the balance between the flammable paraffinic core and the flame-retardant silica shell of PCMs. Overall, bio-based PU-PCM panels show potential for transportation and construction applications owing to their lightweight, insulating, and flame-retardant properties. They offer improved sustainability and thermal-mechanical performance compared to conventional PU panels and flammable PCMs, while supporting circular economy principles by valorising end-of-life WCO
Using Posidonia Oceanica Fibres for the Mechanical Improvement of Sediments
No full textThis study presents the first step of research aiming at investigating the potential use of Posidonia Oceanica (PO) as a natural additive for the mechanical improvement of dredged sediments or soils. Leveraging image analysis methodologies, the correlation between Posidonia needle ball (NB) diameter and fibre length is explored, which is crucial for sediment treatment optimisation. Fibre length estimation utilised imaging analysis, with methods focused on identifying the longest skeleton path in MATLAB© and the maximum length of the bounding ellipse in ImageJ©. Both approaches employed thresholding techniques as image pre-treatment. Results reveal a clear association between ball diameter and fibre length, with skeleton path techniques demonstrating superior precision in fibre length determination. This is important to be able to easily select PO fibres of specific lengths to be added for the mechanical improvement of sediments
Efficient Static Security Assessment of Microgrids Through Practical Feasibility Region Definition
No full textQuantum-Optimal Frequency Estimation of Stochastic ac Fields
No full textResolving frequencies in a time-dependent field is classically limited by the measurement bandwidth. Using tools from quantum metrology and quantum control may overcome this limit, yet the full advantage afforded by entanglement so far remains elusive. Here we map the problem of frequency measurement to that of estimating a global dephasing quantum channel. In this way, we determine the ultimate quantum limits of frequency estimation in stochastic ac sensing. We find exact quantum Fisher information bounds for estimating frequency and frequency differences of stochastic fields. In particular, given two close signals with frequency separation ω_{r}, we find that the quantum Fisher information for the separation estimation is approximately 2/ω_{r}^{2}, i.e., inversely proportional to the separation parameter. The bounds are achievable in certain regimes by superpositions of Dicke states. GHZ states are suboptimal but improve precision over unentangled states, achieving Heisenberg scaling in the low-bandwidth limit. This Letter establishes a robust framework for stochastic ac signal sensing that can be extended to arbitrary time-dependent and stochastic fields
On a zero mass Schrödinger-Bopp-Podolsky system: ground states, nonexistence results and asymptotic behaviour
No full textLabel-Free Detection of Respiratory Syncytial Virus in Clinical Nasopharyngeal Swab Samples Using a Silicon-Nitride Slot Microring Resonator
No full textWe report a silicon-nitride slot-waveguide microring resonator functionalized with monoclonal antibodies against the respiratory-syncytial-virus (RSV) fusion (F) protein for label-free, real-time detection of RSV. The 100 μm-radius slotted ring exhibits a loaded quality factor of 3.5 × 104 and a bulk sensitivity of 275 nm/RIU at 1310 nm. Langmuir fitting of recombinant F-protein titrations (0.05–3 nM) gives an equilibrium dissociation constant KD = 5.4 × 10-2nM and Δλmax = 7.3 nm (R2 = 0.997), yielding a limit-of-detection of 31 pM and a linear dynamic range spanning three orders of magnitude. When challenged with nasopharyngeal swab extracts from RSV-positive patients, the sensor produces wavelength shifts of 0.35–1.45 nm that correlate quantitatively with viral load over > 103-fold concentration range and show 100% concordance with RT-qPCR on three clinical specimens. The wash-free assay requires low volumes of samples and no labeling, and it is fully compatible with disposable CMOS-compatible photonic chips, underscoring its potential for near-patient RSV surveillance during seasonal outbreaks
Machine learning with sub-diffraction resolution in the photon-counting regime
No full textThe resolution of optical imaging is classically limited by the width of the point-spread function, which in turn is determined by the Rayleigh length. Recently, spatial-mode demultiplexing (SPADE) has been proposed as a method to achieve sub-Rayleigh estimation and discrimination of natural, incoherent sources. Here, we show that SPADE yields sub-diffraction resolution in the broader context of image classification. To achieve this goal, we outline a hybrid machine learning algorithm for image classification that includes a physical part and a computational part. The physical part implements a physical pre-processing of the optical field that cannot be simulated without essentially reducing the signal-to-noise ratio. In detail, a spatial-mode demultiplexer is used to sort the transverse field, followed by mode-wise photon detection. In the computational part, the collected data are fed into an artificial neural network for training and classification. As a case study, we classify images from the MNIST dataset after severe blurring due to diffraction. Our numerical experiments demonstrate the ability to classify highly blurred images that would be otherwise indistinguishable by direct imaging without the physical pre-processing of the optical field
